To earn the OCA Java SE 8 Programmer I Certification, you have to know your Java inside and out, and to pass the exam you need to understand the test itself. This book cracks open the questions, exercises, and expectations you’ll face on the OCA exam so you’ll be ready and confident on test day. OCA Java SE 8 Programmer I Certification Guide prepares Java developers for the 1Z0-808 with thorough coverage of Java topics typically found on the exam. Each chapter starts with a list of exam objectives mapped to section numbers, followed by sample questions and exercises that reinforce key concepts. You’ll learn techniques and concepts in multiple ways, including memorable analogies, diagrams, flowcharts, and lots of well-commented code. You’ll also get the scoop on common exam mistakes and ways to avoid traps and pitfalls.
2023-01-10 08:33:56 17.79MB OCA JavaSE Programmer Guide
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应用组合学 Applied Combinatorics 2016 Edition Mitchel T. Keller
2023-01-09 12:29:15 5.24MB 应用组合学
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Yang Gao1, Oscar Beijbom1, Ning Zhang2∗, Trevor Darrell1 †Bilinear models has be
2023-01-07 20:46:27 2.06MB
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The Deep Learning textbook is a resource intended to help students and practitioners enter the field of machine learning in general and deep learning in particular. Table of Contents Chapter 1 Introduction Part I: Applied Math and Machine Learning Basics Chapter 2 Linear Algebra Chapter 3 Probability and Information Theory Chapter 4 Numerical Computation Chapter 5 Machine Learning Basics Part II: Modern Practical Deep Networks Chapter 6 Deep Feedforward Networks Chapter 7 Regularization Chapter 8 Optimization for Training Deep Models Chapter 9 Convolutional Networks Chapter 10 Sequence Modeling: Recurrent and Recursive Nets Chapter 11 Practical Methodology Chapter 12 Applications Part III: Deep Learning Research Chapter 13 Linear Factor Models Chapter 14 Autoencoders Chapter 15 Representation Learning Chapter 16 Structured Probabilistic Models for Deep Learning Chapter 17 Monte Carlo Methods Chapter 18 Confronting the Partition Function Chapter 19 Approximate Inference Chapter 20 Deep Generative Models
2023-01-07 16:10:50 77.75MB Deep Learning
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经典的密码和网络攻防的书!适合专业和非专业的本科与研究生阅读!
2023-01-06 15:01:55 4.08MB 密码学
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针对序列图像超分辨率重建非局部均值(non-local means,NLM)算法重建结果图像边缘区域过平滑的问题,提出了一种局部参数自适应改进方法。将整幅图像划分为图像子块,然后根据图像子块平均像素信息计算出其对应的滤波参数,这样有助于减少因整幅图像使用统一滤波参数而导致的某些高频信息的丢失。实验结果表明,与经典NLM重构算法相比,改进算法重建出的结果图像的轮廓边缘更清晰,字符辨识度更高;在算法实现方面,图像重构程序在CPU/GPU平台上实现,使用GPU并行化加速的程序比单CPU运算的程序,加速比最高可达
2023-01-04 13:47:08 1.71MB 工程技术 论文
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清华大学离散数学2016期末考试
2023-01-04 02:32:52 655KB
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2016—2017年以来我...管产业的运行情况及技术进步_王晓香
2022-12-29 17:18:52 297KB
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国科大模式识别刘成林期末试卷2016-2019
2022-12-26 15:26:19 1.98MB 刘成林 国科大 模式识别 期末试卷
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2016网站建设实习报告   毕业实习是结束大学课堂理论学习后的一次极为重要的实践性环节。通过实习,可以 让学生对专业知识有一个更全面、更深刻的理解,为学生走上工作岗位,完成人生角色 转换,打下良好基础。   以前人毕业实习完全由学校组织,学生人数不多,基本能够做到"真题真做",专业对 口,将实习和就业分离,含有很大的统招统分的色彩。但面对高校扩招,尽管学校花费 了很大的力量联系实习单位,仍难以安排"大兵团"的实习。随着高校扩招和就业双向选择 的实施,传统的实习模式已不能适应学生及用人单位的要求。新形势下,学生的自主实习 越来越多,也给实习管理来很多问题。实习生管理不到位。实习生都是自主联系,或者 是小组实习,一般的实习小组都小于10人,学校不可能每个实习点都有指导教师监督, 实习生在不在岗,实习情况如何,学校管理部门难以知晓,检查起来也有很多困难。"非 典"期间,学校需要统计在外实习学生人数及分布,结果学工和教务得出的数据差别很大 。这必然带来学生管理问题,这种处于无人管理状况的毕业实习,学生很容易钻空子, 由于就业的压力学生不安心实习,不认真实习或者干脆就不实习的情况大有人在,这样
2022-12-23 18:15:58 27KB 文档资料
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